Use Those Buzzwords: Making Data More Approachable

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Numerous articles underscore just how important it is to avoid using jargon and buzzwords in your work. Experts are right to point out that buzzwords can often alienate people and muddle your message. I absolutely agree…with one caveat. When it comes to data, you should embrace buzzwords.

Perhaps counterintuitively, using jargon is one of the best tools at your disposal for building a data culture. As data-minded professionals, it is up to us to help everyone feel comfortable with the concepts and ideas that make up an effective data strategy.

After all, data is a language. To learn a new language, we first have to listen. Speaking the language of data so that others can hear it will help individuals across your organization to feel competent and confident enough to use it themselves.

Additionally, by regularly speaking the language of data you also help to systematically desensitize people to data concepts. This may seem nefarious, but hear me out. Data concepts bring with them a lot of fear and uncertainty. When we tip-toe around advanced ideas and use euphemisms, we reinforce the stigma around them. When we speak about them directly, with appropriate context and definition, we lift that veil and make them less scary. When we speak the language of data, we show others how accessible data and data strategy can be.

Here is one practical step you can take to spread the language of data in your organization: Use data buzzwords. All. The. Time.

I am the Chief Data Nerd at work—and I love it. I own it. I use data buzzwords as often as I can and in every context. This comes naturally to me but I am also going out of my way to drop data words into my everyday conversations with my colleagues. Here are some examples:

Scenario 1: I am sitting and eating lunch with my colleagues, and they are talking about a TV show. I inevitably have not seen this show because I rarely watch TV. Everyone laughs about a certain scene, and I will throw in “Well, I guess I'm the outlier in the room since I haven't seen it!” In this case, the meaning of “outlier” is implicit from the context of my comment. An outlier is a data point that is set apart from the rest.

Scenario 2: I am sitting in a team meeting in which we are discussing a logic model. I can tell there is a disconnect somewhere, and say: “To clarify, are we talking about this as an output, meaning an immediate effect of this intervention, or as an outcome, meaning something we might hope to see in the more medium or long term?” In a scenario like this one, I go ahead and offer the definition of the buzzwords as part of my question.

Scenario 3: We have an internal newsletter, and I often try to include a link to an article that uses a lot of data buzzwords. I don't know if my teammates even open the article, but they see the title and my short explanation (which includes even more data buzzwords) about why I think the article is useful.

Other favorites of mine include:

  • Axis: A reference line drawn on a graph (X [horizontal axis] and Y [vertical axis]).
  • Range: The difference between the highest and lowest value (or, a less technical definition meaning the full spectrum of your data points from highest to lowest).
  • Frequency: How often data points occur in your data set.

For many nonprofit professionals, data can be mysterious and even intimidating. Fortunately, with a few intentional steps, we can help more people to “speak data” and, most importantly, leverage it to strengthen our work.

Data has the potential to help us move at a faster pace towards our communal goals. Now all we have to do is increase mean data confidence scores among our colleagues and peers (see how I did that?).


Want to learn more about how you can integrate data into your work? Check out the Data Playbook, a resource designed to support nonprofit professionals on their data journey!

Rella Kaplowitz is the Senior Program Officer for Evaluation and Learning at the Charles and Lynn Schusterman Family Foundation. Rella is currently working on innovative ways to speed up the learning curve in data language acquisition.